text: import os
  import glob
  import tqdm
  import torch
  import argparse
  from scipy.io.wavfile import write
  import numpy as np
  from model.generator import ModifiedGenerator
  from utils.hparams import HParam, load_hparam_str
  from denoiser import Denoiser
  
  MAX_WAV_VALUE = 32768.0
  
  
  def main(args):
      checkpoint = torch.load(args.checkpoint_path)
      if args.config is not None:
          hp = HParam(args.config)
      else:
          hp = load_hparam_str(checkpoint['hp_str'])
  
      model = ModifiedGenerator(hp.audio.n_mel_channels, hp.model.n_residual_layers,
                          ratios=hp.model.generator_ratio, mult = hp.model.mult,
                          out_band = hp.model.out_channels).cuda()
      model.load_state_dict(checkpoint['model_g'])
      model.eval(inference=True)
  
      with torch.no_grad():
          mel = torch.from_numpy(np.load(args.input))
          if len(mel.shape) == 2:
              mel = mel.unsqueeze(0)
          mel = mel.cuda()
          audio = model.inference(mel)
  
          audio = audio.squeeze(0)  # collapse all dimension except time axis
          if args.d:
              denoiser = Denoiser(model).cuda()
              audio = denoiser(audio, 0.01)
          audio = audio.squeeze()
          audio = audio[:-(hp.audio.hop_length*9)]
          audio = MAX_WAV_VALUE * audio
          audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE-1)
          audio = audio.short()
          audio = audio.cpu().detach().numpy()
  
          out_path = args.input.replace('.npy', '_gen_epoch%04d.wav' % checkpoint['epoch'])
          write(out_path, hp.audio.sampling_rate, audio)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('-c', '--config', type=str, default=None,
                          help="yaml file for config. will use hp_str from checkpoint if not given.")
      parser.add_argument('-p', '--checkpoint_path', type=str, required=True,
                          help="path of checkpoint pt file for evaluation")
      parser.add_argument('-i', '--input', type=str, required=True,
                          help="directory of mel-spectrograms to invert into raw audio. ")
      parser.add_argument('-d', default=True, action='store_true', help="denoising ")
      args = parser.parse_args()
  
      main(args)
  